Abstract
This chapter emphasized the pivotal role of GIS in environmental decision-making by exploring spatial data collection methods, including GPS, remote sensing, LiDAR, and drones, highlighting their contributions to understanding environmental attributes. Spatial data integration and management within GIS are discussed as an efficient approach to data storage, retrieval, analysis, and modeling that facilitates evidence-based decision-making. Public engagement and communication using GIS can foster stakeholder involvement. GIS's relevance in shaping environmental policies and regulations is emphasized, covering habitat preservation, water resource management, air quality regulation, and more. It also discussed the concept of SDI and its role in addressing environmental challenges. The challenges and future directions of GIS in environmental planning and management are addressed, covering data quality, privacy, capacity building, and public engagement. Future prospects include AI, real-time data integration, cloud-based GIS, 3D and virtual reality, and crowdsourcing are also endorsed.
TopEnvironmental Data Collection And Integration
Environmental data collection and integration serve as the bedrock for informed environmental decision-making, enabling a holistic understanding of our ecosystems.
Key Terms in this Chapter
Geographic Information System (GIS): A GIS is a computer system that captures, stores, analyzes, and displays geographic information. It integrates various types of data with spatial locations, allowing users to visualize, understand, and query relationships between geographic features and phenomena.
Machine Learning (ML): A subfield of AI, machine learning focuses on algorithms that can learn from data without explicit programming. Machine learning algorithms are trained on large datasets to identify patterns and relationships within the data.
Spatial Analysis: This refers to a set of techniques used to analyze the spatial relationships, patterns, and trends of geographic data. Spatial analysis tools within GIS software allow users to identify clusters of features, calculate distances and network connectivity, and model spatial processes.
Spatial Data Infrastructure (SDI): An SDI is a framework that facilitates the creation, sharing, and use of spatial data across different organizations and platforms. It establishes standards for data formats, quality, and accessibility, ensuring that spatial data from various sources can be integrated and analyzed effectively.
Artificial Intelligence (AI): AI is a branch of computer science concerned with creating intelligent machines capable of mimicking human cognitive functions such as learning and problem-solving.
Participatory GIS (PGIS): This approach to GIS involves the active participation of stakeholders, such as citizens or community members, in collecting, analyzing, and visualizing geographic information. PGIS empowers communities to contribute their local knowledge and perspectives to decision-making processes.